Modern vehicles, especially automobiles, increasingly provide autonomous driving and driving assistance systems such as blind spot monitors, automatic parking, and automatic navigation. Testing and validating the autonomous driving and the driving assistance systems, however, is highly complex and can require prolonged road testing (e.g., millions of hours and miles). The testing and validation effort is multiplied when considering that updates to the autonomous driving and the driving assistance systems can require revalidation, and separate validation may be required for different vehicles types.
In addition, it is also difficult to secure sufficient training data for learning autonomous driving systems and the driving assistance systems of autonomous vehicles.
Therefore, a method for training, testing, and validating the autonomous driving systems and the driving assistance systems of the autonomous vehicles operating in a virtual driving environment in which an actual driving situation is virtualized has been proposed.
In order to train, test, and validate the autonomous driving systems and the driving assistance systems of the autonomous vehicles in the virtual driving environment, scenarios for information on surroundings in the virtual driving environment should be provided.
However, in a conventional virtual driving environment, because scenarios only for specific traffic conditions are provided, a variety of sophisticated scenarios as in the actual driving environment cannot be provided.